Proteomic Analysis, Immune Dysregulation, and ... - ACS Publications

Subscriber access provided by TUFTS UNIV

Article

Proteomic Analysis, Immune Dysregulation and Pathway Interconnections with Obesity Carly B Garrison, Kristin J Lastwika, Yuzheng Zhang, Christopher I. Li, and Paul D Lampe J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00611 • Publication Date (Web): 21 Oct 2016 Downloaded from http://pubs.acs.org on October 23, 2016

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Proteome Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Proteome Research

Proteomic Analysis, Immune Dysregulation and Pathway Interconnections with Obesity Carly B. Garrison1‡, Kristin J. Lastwika1‡, Yuzheng Zhang1, Christopher I. Li1, Paul D. Lampe1* 1Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA KEYWORDS: Proteomics, Pathways, Antibody Array, Obesity, Inflammation, Immune System

ABSTRACT: Proteomic studies can offer information on hundreds to thousands of proteins and potentially provide researchers with a comprehensive understanding of signaling response during stress and disease. Large datasets, such as those obtained in high-dimensional proteomic studies, can be leveraged for pathway analysis to discover or describe the biological implications of clinical disease states. Obesity is a worldwide epidemic that is considered a risk factor for numerous other diseases. We performed analysis on plasma proteomic data from 3 separate sample sets of post-menopausal women to identify the pathways that are altered in subjects with a high body mass index (BMI) compared to normal BMI. We found many pathways consistently and significantly associated with inflammation dysregulated in plasma from obese/overweight subjects compared to plasma from normal BMI subjects. These pathways indicate alterations of soluble inflammatory regulators, cellular stress, and metabolic dysregulation. Our results

ACS Paragon Plus Environment

1


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 38

highlight the importance of high-dimensional pathway analysis in complex diseases as well as provide information on the interconnections between pathways that are dysregulated with obesity. Specifically, overlap of obesity related pathways with those activated during cancer and infection could help describe why obesity is a risk factor for disease and help devise treatment options that mitigate its effect.

Introduction High-dimensional -omics studies, such as transcriptomics and proteomics have transformed biomedical research by enabling comprehensive real-time monitoring of a biological system. Proteomic studies are generally performed in one of two ways, with mass spectrometry (MS) or immunoassays.

New developments have allowed both methods to provide information on

thousands of proteins at a given point in time. Thus, these new levels of proteomic coverage allow a more comprehensive reporting of disease etiology than was previously only accessible via mRNA expression array analysis. Furthermore, since the transcriptome and proteome can vary significantly and proteins can be regulated and modified post-translationally, proteomic analysis can yield a more comprehensive picture of actual cellular status. Additionally, by identifying pathways that differ between two conditions, one can have more explanatory power of the difference between the two states than with individual proteins alone. By examining the sum effect of the different pathways, a comprehensive overview of disease can be obtained. Validation of the up or down regulated pathways in multiple studies to identify the most important biological pathways can yield conclusions about even a complicated disease process like obesity. Obesity is currently a worldwide epidemic, more prevalent in developed countries, that shows little evidence for declining or plateauing1,2. In the United States, more than one-third of adults


2

Page 3 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(78.6 million) and 17% of children (12.7 million) are obese3. Worldwide there are more than 1.9 billion overweight and over 600 million obese adults4. Obesity is included in the global noncommunicable diseases that are being targeted for change by the World Health Organization, with the intention of halting the rise of obesity to its 2010 level by 20252. Body-mass index (BMI) is clinically used to identify individuals who may have high body fat. BMI can help to screen patients for certain weight categories, such as overweight or obese, but is not a singular diagnostic tool for the health of an individual5. A high BMI is considered a risk factor for cardiovascular disease, kidney disease, diabetes, musculoskeletal disorders, and some cancers6– 12

. Males and females differ in how and where they store body fat and post-menopausal women

are more likely to be obese then pre-menopausal women13. Adipose tissue is an important part of the endocrine system that helps to maintain a balance of energy homeostasis and immune system reactivity by regulating lipid storage and controlling the production and secretion of a wide range of adipokines and cytokines14. Additionally, in postmenopausal women, adipose tissue is the major source of steroid hormone production with estrogen regulating body adiposity and fat distribution15 and potentially modifying risk for disease. Several proteomic studies have been employed in multiple tissue types such as adipose tissue, isolated adipocytes16, and plasma17 to analyze gene expression changes in obese patients. These studies have identified extensive upregulation of inflammatory pathways. It is thought that these alterations induce chronic inflammation which contributes to the development of the many obesity-related illnesses including type-2 diabetes (T2D), cardiovascular disease and cancer18. To further examine the biology behind the adverse inflammatory response in obesity, we utilized a high-density antibody microarray platform to examine plasma from post-menopausal women with a wide range of BMI in one autoantibody and three proteomic studies.


3


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 38

Experimental Section Clinical Samples: Plasma samples from the Women’s Health Initiative (WHI) Observational Study, a prospective cohort of 93,676 post-menopausal women enrolled from 1993 to 1998 in the United States were used for these studies19,20. Plasma from women with no prior history of any type of cancer and no cancer diagnosis two years after collection were used in four separate studies and profiled on customized antibody arrays21–23. The four studies were comprised of 208, 308, 322, and 86 samples and were well matched in age across studies (between 50 and 79 years of age for all studies) (Table 1). The use of human samples and all patient information provided was done so in accordance with the Institutional Review Board at the Fred Hutchinson Cancer Research Center. Antibody Arrays: Arrays were populated with more than 3000 distinct full-length antibodies primarily from SDIX (now sold by Novus), Aviva Biosciences, R&D Systems, Abnova, SigmaAldrich, and a few other manufacturers to more than 2100 different proteins involved in a diverse array of signaling pathways. Although there was significant overlap in content, each study had some variation in the specific antibodies used. A detailed list of the antibodies used in study 3 has previously been published24. Details on array fabrication have been previously reported25,26. Proteomics: Relative protein levels were detected as previously described23,25,27,28. Briefly, albumin and IgG were depleted from plasma. The depleted plasma was concentrated to its original volume, measured for total protein concentration, labeled with the amine reactive dyes Cy3- and Cy5-maleimide and unincorporated dye was removed. Individual Cy5-labeled subject samples were incubated with an equal amount of Cy3-labeled reference (a common pool of plasma comprised of samples collected from 7 women aged 45–72 years was used as a reference


4

Page 5 of 38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


for all samples). Labeled lysates were incubated on arrays for 90 minutes, washed serially to remove excess dye, and slides were scanned in an Axon GenePix 4000B microarray scanner and data extracted using GenePix Pro 6.0 software (Molecular Devices, Sunnyvale, CA). All proteomic data is publically available in the National Center for Biotechnology Information’s (NCBI) Gene Expression Omnibus (GEO) repository. Autoantibody-antigen: Autoantibody-antigen complexes were detected as previously described24. Briefly, undepleted human plasma was diluted 1:80, pipetted onto the array at the microarray/coverslip junction and incubated for 60 min at room temperature. Autoantigenantibody complexes were detected after incubation with Alexa Fluor 546-goat anti-human IgG and Alexa Fluor 647-goat anti-human IgM for 60 min at room temperature. As a control to determine background levels of signal, the arrays were incubated with just secondary antibody (no plasma added) and the resulting signals were used for background subtraction. Finally, the slides were scanned on an Axon GenePix 4000B microarray scanner and data was extracted using GenePix Pro 6.0 software. Autoantibody-antigen data (Study 4) is publically available in the GEO repository. Statistical Analysis: For each protein study, fold change of signal (red channel) compared to reference (green channel), the M value, was calculated as log2(Rc/Gc); where Rc is red corrected and Gc is green corrected for each antibody spot27,29 (using the normexp background correction method developed by Smyth)30. Technical sources of variation were normalized using loess procedures developed for microarrays, including within-array print-tip loess and between-arrays quartile normalization. Following normalization, triplicate features were summarized using their median. M values were further normalized using linear regression to remove the systematic bias due to factors such as hybridization day, hormone replacement therapy, age and ethnicity. For


5


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 6 of 38

autoantibody-antigen complex analysis, cases and controls red and green spot intensities are analyzed separately for IgG and IgM specific autoantibody content24. Pathway Analysis: To perform pathway analysis, only proteins that were significantly altered (p

Proteomic Analysis, Immune Dysregulation, and ... - ACS Publications

Recommend Documents